import pandas as pd
import numpy as np
from collections import Counter
from datetime import datetime, timedelta
import logging
import matplotlib.pyplot as plt
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import openai
import json

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class TrendEngine:
    """行业趋势分析引擎"""
    
    def __init__(self, config):
        self.config = config
        openai.api_key = self.config["openai_api_key"]
        openai.base_url = self.config.get("openai_base_url", "https://api.openai.com/v1/")
        
    def load_data(self, jobs_file, trends_file):
        """加载职位和趋势数据
        
        Args:
            jobs_file (str): 职位数据文件路径
            trends_file (str): 趋势数据文件路径
            
        Returns:
            tuple: (jobs_df, trends_df)
        """
        try:
            jobs_df = pd.read_csv(jobs_file)
            trends_df = pd.read_csv(trends_file)
            logger.info(f"加载了 {len(jobs_df)} 条职位数据和 {len(trends_df)} 条趋势数据")
            return jobs_df, trends_df
        except Exception as e:
            logger.error(f"数据加载错误: {str(e)}")
            return pd.DataFrame(), pd.DataFrame()
    
    def analyze_hot_skills(self, jobs_df, top_n=20):
        """分析热门技能需求
        
        Args:
            jobs_df (DataFrame): 职位数据
            top_n (int): 返回的热门技能数量
            
        Returns:
            dict: 热门技能及其出现频率
        """
        # 合并所有职位描述
        all_descriptions = " ".join(jobs_df['requirements'].dropna().tolist())
        
        # 使用LLM提取技能
        skills = self._extract_skills_with_llm(all_descriptions)
        
        # 计算频率
        skill_counter = Counter(skills)
        
        # 获取前N个热门技能
        top_skills = {skill: count for skill, count in skill_counter.most_common(top_n)}
        
        return top_skills
    
    def _extract_skills_with_llm(self, text):
        """使用LLM从文本中提取技能"""
        try:
            # 文本太长，只取前10000个字符
            text = text[:10000]
            
            response = openai.chat.completions.create(
                model=self.config["llm_model"],
                messages=[
                    {"role": "system", "content": "你是一位人才招聘专家，请从职位描述中提取关键技能需求。"},
                    {"role": "user", "content": f"请从以下职位描述中提取所有技能需求，以JSON数组格式返回，例如[\"Python\", \"数据分析\"]:\n\n{text}"}
                ],
                temperature=0.3,
                response_format={"type": "json_object"}
            )
            
            result = response.choices[0].message.content
            skills_json = json.loads(result)
            
            if "skills" in skills_json:
                return skills_json["skills"]
            return []
            
        except Exception as e:
            logger.error(f"技能提取错误: {str(e)}")
            return []
    
    def identify_growth_areas(self, jobs_df, trends_df):
        """识别增长领域和趋势
        
        Args:
            jobs_df (pd.DataFrame): 职位数据
            trends_df (pd.DataFrame): 趋势数据
            
        Returns:
            dict: 增长领域信息
        """
        logger.info("识别行业增长领域和趋势...")
        
        # 检查趋势数据中的列
        available_columns = trends_df.columns.tolist()
        logger.info(f"趋势数据包含以下列: {available_columns}")
        
        # 分析趋势数据中的行业增长率
        growth_by_industry = {}
        
        if 'industry' in available_columns and 'growth_rate' in available_columns:
            # 按行业分组计算平均增长率
            industry_growth = trends_df.groupby('industry')['growth_rate'].mean()
            
            for industry, growth in industry_growth.items():
                growth_by_industry[industry] = {
                    'growth_rate': growth,
                    'impact': 'high' if growth > 60 else ('medium' if growth > 40 else 'low')
                }
        
        # 提取每个行业的热门趋势
        industry_trends = {}
        
        if 'industry' in available_columns and 'trend' in available_columns:
            # 按行业分组
            for industry, group in trends_df.groupby('industry'):
                # 按增长率排序，提取前3个趋势
                top_trends = group.sort_values('growth_rate', ascending=False).head(3)
                
                trends_list = []
                for _, row in top_trends.iterrows():
                    trend_info = {
                        'name': row['trend'],
                        'growth_rate': row['growth_rate']
                    }
                    
                    # 添加时间框架（如果存在）
                    if 'timeframe' in available_columns:
                        trend_info['timeframe'] = row['timeframe']
                    
                    # 添加所需技能（如果存在）
                    if 'required_skills' in available_columns:
                        trend_info['skills'] = row['required_skills'].split(', ') if isinstance(row['required_skills'], str) else []
                    
                    trends_list.append(trend_info)
                
                industry_trends[industry] = trends_list
        
        # 从职位数据中获取行业需求
        industry_demand = {}
        
        if 'industry' in jobs_df.columns:
            industry_counts = jobs_df['industry'].value_counts()
            total_jobs = len(jobs_df)
            
            for industry, count in industry_counts.items():
                if isinstance(industry, str) and industry.strip():  # 确保行业名称有效
                    percentage = (count / total_jobs) * 100
                    industry_demand[industry] = {
                        'job_count': int(count),
                        'percentage': round(percentage, 2)
                    }
        
        # 提取所需技能
        required_skills = {}
        
        if 'required_skills' in available_columns:
            all_skills = []
            for skills in trends_df['required_skills'].dropna():
                if isinstance(skills, str):
                    all_skills.extend([s.strip() for s in skills.split(',')])
            
            skill_counts = {}
            for skill in all_skills:
                if skill:
                    skill_counts[skill] = skill_counts.get(skill, 0) + 1
            
            # 按出现频率排序
            sorted_skills = sorted(skill_counts.items(), key=lambda x: x[1], reverse=True)
            required_skills = {skill: count for skill, count in sorted_skills[:20]}
        
        # 整合结果
        result = {
            'industry_growth': growth_by_industry,
            'industry_trends': industry_trends,
            'industry_demand': industry_demand,
            'required_skills': required_skills
        }
        
        logger.info(f"已识别 {len(growth_by_industry)} 个增长行业和 {sum(len(trends) for trends in industry_trends.values())} 个热门趋势")
        
        return result
    
    def _extract_growth_areas_with_llm(self, text):
        """使用LLM从文本中提取增长领域"""
        try:
            # 文本太长，只取前15000个字符
            text = text[:15000]
            
            response = openai.chat.completions.create(
                model=self.config["llm_model"],
                messages=[
                    {"role": "system", "content": "你是一位行业分析专家，请从行业报告中提取未来3年的高增长领域。"},
                    {"role": "user", "content": f"请从以下行业报告总结中识别未来3年的高增长领域，以JSON数组格式返回，例如[\"AI医疗\", \"绿色能源\"]:\n\n{text}"}
                ],
                temperature=0.3,
                response_format={"type": "json_object"}
            )
            
            result = response.choices[0].message.content
            areas_json = json.loads(result)
            
            if "growth_areas" in areas_json:
                return areas_json["growth_areas"]
            return []
            
        except Exception as e:
            logger.error(f"增长领域提取错误: {str(e)}")
            return []
    
    def _calculate_avg_salary(self, jobs_df):
        """计算平均薪资"""
        if jobs_df.empty:
            return "未知"
            
        # 处理薪资格式，如"20k-30k"
        salary_ranges = jobs_df['salary'].dropna().tolist()
        if not salary_ranges:
            return "未知"
            
        total_min = 0
        total_max = 0
        count = 0
        
        for salary in salary_ranges:
            # 解析薪资范围
            try:
                if 'k' in salary.lower() or 'K' in salary:
                    # 处理"20k-30k"格式
                    parts = salary.lower().replace('k', '').split('-')
                    if len(parts) == 2:
                        min_val = float(parts[0].strip())
                        max_val = float(parts[1].strip())
                        total_min += min_val
                        total_max += max_val
                        count += 1
            except:
                continue
                
        if count == 0:
            return "未知"
            
        avg_min = total_min / count
        avg_max = total_max / count
        
        return f"{avg_min:.1f}k-{avg_max:.1f}k"
    
    def _calculate_trend_score(self, area, trends_df):
        """计算领域趋势分数"""
        if trends_df.empty:
            return 0
            
        # 计算该领域在报告中的提及频率
        mention_count = 0
        for summary in trends_df['summary'].dropna():
            if area.lower() in summary.lower():
                mention_count += 1
                
        # 计算提及率
        mention_rate = mention_count / len(trends_df)
        
        # 简单的趋势分数（0-10）
        trend_score = min(10, int(mention_rate * 10 * 3))  # 乘以3增加权重
        
        return trend_score
        
    def generate_trend_report(self, hot_skills, growth_areas):
        """生成趋势报告
        
        Args:
            hot_skills (dict): 热门技能
            growth_areas (dict): 增长领域
            
        Returns:
            str: 趋势报告内容
        """
        timestamp = datetime.now().strftime('%Y-%m-%d')
        
        report = f"# 行业趋势分析报告 ({timestamp})\n\n"
        
        # 热门技能部分
        report += "## 热门技能需求排名\n\n"
        if isinstance(hot_skills, dict) and hot_skills:
            for i, (skill, count) in enumerate(hot_skills.items(), 1):
                report += f"{i}. **{skill}**: {count} 次提及\n"
        else:
            report += "未能获取热门技能数据\n"
        
        # 提取行业增长数据
        report += "\n## 行业增长趋势\n\n"
        report += "| 行业 | 增长率 | 影响程度 |\n"
        report += "|------|--------|----------|\n"
        
        if 'industry_growth' in growth_areas and growth_areas['industry_growth']:
            for industry, data in growth_areas['industry_growth'].items():
                growth_rate = data.get('growth_rate', 'N/A')
                impact = data.get('impact', 'N/A')
                report += f"| {industry} | {growth_rate:.1f}% | {impact} |\n"
        else:
            report += "| 无数据 | N/A | N/A |\n"
        
        # 行业趋势部分
        report += "\n## 行业热门趋势\n\n"
        
        if 'industry_trends' in growth_areas and growth_areas['industry_trends']:
            for industry, trends in growth_areas['industry_trends'].items():
                report += f"### {industry}\n\n"
                if trends:
                    for trend in trends:
                        name = trend.get('name', 'N/A')
                        growth_rate = trend.get('growth_rate', 'N/A')
                        timeframe = trend.get('timeframe', 'N/A')
                        
                        report += f"- **{name}**\n"
                        report += f"  - 增长率: {growth_rate:.1f}%\n"
                        report += f"  - 时间范围: {timeframe}\n"
                        
                        if 'skills' in trend and trend['skills']:
                            report += f"  - 相关技能: {', '.join(trend['skills'])}\n"
                        
                        report += "\n"
                else:
                    report += "无热门趋势数据\n\n"
        else:
            report += "无行业趋势数据\n\n"
        
        # 行业需求部分
        report += "## 行业职位需求\n\n"
        report += "| 行业 | 职位数量 | 占比 |\n"
        report += "|------|----------|------|\n"
        
        if 'industry_demand' in growth_areas and growth_areas['industry_demand']:
            for industry, data in growth_areas['industry_demand'].items():
                job_count = data.get('job_count', 0)
                percentage = data.get('percentage', 0)
                report += f"| {industry} | {job_count} | {percentage:.1f}% |\n"
        else:
            report += "| 无数据 | 0 | 0% |\n"
        
        # 热门技能需求
        report += "\n## 热门技能需求\n\n"
        
        if 'required_skills' in growth_areas and growth_areas['required_skills']:
            for i, (skill, count) in enumerate(growth_areas['required_skills'].items(), 1):
                report += f"{i}. **{skill}**: {count} 次提及\n"
        else:
            report += "无热门技能数据\n"
        
        # 保存报告
        report_filename = f"data/trend_report_{datetime.now().strftime('%Y%m%d%H%M%S')}.md"
        try:
            with open(report_filename, 'w', encoding='utf-8') as f:
                f.write(report)
            logger.info(f"趋势报告已保存至 {report_filename}")
        except Exception as e:
            logger.error(f"报告保存失败: {str(e)}")
            
        return report

    def analyze_industry_trends(self, job_description, region="CN"):
        """分析职位描述，提取行业趋势信息
        
        Args:
            job_description (str): 职位描述文本
            region (str): 地区代码，默认CN(中国)，可选US(美国)，EU(欧盟)，GLOBAL(全球)
        
        Returns:
            dict: 包含行业分析结果的字典
        """
        # 重新定义地区映射，确保一致性
        region_name_map = {
            "CN": "中国",
            "US": "美国",
            "EU": "欧盟",
            "JP": "日本",
            "UK": "英国", 
            "IN": "印度",
            "GLOBAL": "全球"
        }
        
        currency_map = {
            "CN": "￥",
            "US": "美元",
            "EU": "欧元",
            "JP": "日元",
            "UK": "英镑",
            "IN": "卢比",
            "GLOBAL": "美元"
        }
        
        salary_format_map = {
            "CN": "xxK/月或xx万/年",
            "US": "xx万美元/年",
            "EU": "xx万欧元/年", 
            "JP": "xx万日元/月",
            "UK": "xx万英镑/年",
            "IN": "xx万卢比/年",
            "GLOBAL": "xx万美元/年"
        }
        
        region_name = region_name_map.get(region, "中国")
        currency = currency_map.get(region, "￥")
        salary_format = salary_format_map.get(region, "xxK/月")
        
        # 为全球分析设置简化提示词
        if region == "GLOBAL":
            prompt = f"""
            分析以下职位描述，提供详细的{region_name}范围内行业趋势分析报告，要求以中文JSON格式返回：
            
            职位描述：{job_description}
            
            请提供{region_name}市场的分析，使用中文回答，包含以下精简字段：
            1. industry: 行业名称
            2. description: 行业在{region_name}市场的详细描述
            3. market_size: {region_name}市场规模估计（{currency}）
            4. growth_rate: {region_name}市场年增长率
            5. hot_skills: [技能列表，每个技能包含name和importance字段]
            6. outlook: {region_name}行业前景分析
            7. opportunities: [主要机遇列表]
            8. challenges: [主要挑战列表]
            
            请确保回复为中文，格式为有效的JSON格式，不包含其他文字说明。
            """
        else:
            # 标准提示词，但明确要求使用中文回答
            prompt = f"""
            分析以下职位描述，提供详细的{region_name}地区行业趋势分析报告，要求以JSON格式返回且内容必须为中文：
            
            职位描述：
            {job_description}
            
            请专门分析{region_name}市场（不是中国市场），您必须使用中文回答，包含以下字段：
            1. industry: 行业名称
            2. description: 行业在{region_name}市场的详细描述（至少200字）
            3. market_size: {region_name}市场规模估计（使用{currency}）
            4. growth_rate: {region_name}市场年增长率
            5. job_demand: {region_name}岗位需求程度
            6. avg_salary: {region_name}平均薪资范围（使用{salary_format}形式）
            
            7. industry_stage: {{
               "stage": "{region_name}市场发展阶段（如起步期/成长期/成熟期/转型期等）",
               "characteristics": ["{region_name}该阶段特点1", "特点2", ...]
            }}
            
            8. hot_skills: [
               {{
                  "name": "技能名称1",
                  "description": "{region_name}市场详细描述（至少50字）",
                  "importance": "高/中/低",
                  "growth_trend": "上升/稳定/下降",
                  "applications": "{region_name}应用场景",
                  "learning_difficulty": "学习难度"
               }},
               {{同上格式}}...
            ]
            
            9. emerging_skills: [同上格式的新兴技能列表]
            
            10. skill_trends: {{
                "技能名1": {{
                    "trend_data": {{
                        "previous_year": 数值,
                        "last_year": 数值,
                        "current_year": 数值,
                        "next_year": 数值
                    }}
                }},
                ...更多技能
            }}
            
            11. salary_trends: {{
                "entry_level": "{region_name}初级薪资范围",
                "mid_level": "{region_name}中级薪资范围",
                "senior_level": "{region_name}高级薪资范围"
            }}
            
            12. salary_factors: [
                {{
                    "name": "{region_name}影响因素1",
                    "description": "详细描述"
                }},
                ...更多因素
            ]
            
            13. job_demand_trends: {{
                "yearly_change": {{
                    "2021": 数值,
                    "2022": 数值,
                    "2023": 数值,
                    "2024": 数值
                }},
                "peak_periods": ["{region_name}高峰期1", "高峰期2"]
            }}
            
            14. region_distribution: {{
                "top_cities": [
                    {{
                        "name": "{region_name}城市1",
                        "percentage": "占比",
                        "dominant_industries": ["主导行业1", "主导行业2"],
                        "avg_salary": "平均薪资范围"
                    }},
                    {{
                        "name": "{region_name}城市2",
                        "percentage": "占比",
                        "dominant_industries": ["主导行业1", "主导行业2"],
                        "avg_salary": "平均薪资范围"
                    }},
                    {{
                        "name": "{region_name}城市3",
                        "percentage": "占比",
                        "dominant_industries": ["主导行业1", "主导行业2"],
                        "avg_salary": "平均薪资范围"
                    }},
                    {{
                        "name": "{region_name}城市4",
                        "percentage": "占比",
                        "dominant_industries": ["主导行业1", "主导行业2"],
                        "avg_salary": "平均薪资范围"
                    }},
                    {{
                        "name": "{region_name}城市5",
                        "percentage": "占比",
                        "dominant_industries": ["主导行业1", "主导行业2"],
                        "avg_salary": "平均薪资范围"
                    }},
                    {{
                        "name": "{region_name}城市6",
                        "percentage": "占比",
                        "dominant_industries": ["主导行业1", "主导行业2"],
                        "avg_salary": "平均薪资范围"
                    }},
                    {{
                        "name": "{region_name}城市7",
                        "percentage": "占比",
                        "dominant_industries": ["主导行业1", "主导行业2"],
                        "avg_salary": "平均薪资范围"
                    }},
                    {{
                        "name": "{region_name}城市8",
                        "percentage": "占比",
                        "dominant_industries": ["主导行业1", "主导行业2"],
                        "avg_salary": "平均薪资范围"
                    }},
                    ...更多{region_name}主要城市（至少10个城市）
                ],
                "regional_features": [
                    {{
                        "region": "{region_name}区域名称",
                        "characteristics": "区域特点详细描述",
                        "industrial_focus": ["重点产业1", "重点产业2", "重点产业3"],
                        "development_stage": "发展阶段",
                        "policy_advantages": "政策优势",
                        "talent_environment": "人才环境描述"
                    }},
                    ...更多区域
                ]
            }}
            
            15. competition_analysis: {{
                "major_companies": [
                    {{
                        "name": "{region_name}公司名称1",
                        "description": "公司描述",
                        "type": "公司类型",
                        "rank": 数字排名,
                        "is_unicorn": true/false, 
                        "market_value": "市值/估值（如有）"
                    }},
                    {{
                        "name": "{region_name}公司名称2",
                        "description": "公司描述",
                        "type": "公司类型",
                        "rank": 数字排名,
                        "is_unicorn": true/false, 
                        "market_value": "市值/估值（如有）"
                    }},
                    {{
                        "name": "{region_name}公司名称3",
                        "description": "公司描述",
                        "type": "公司类型",
                        "rank": 数字排名,
                        "is_unicorn": true/false, 
                        "market_value": "市值/估值（如有）"
                    }},
                    {{
                        "name": "{region_name}公司名称4",
                        "description": "公司描述",
                        "type": "公司类型",
                        "rank": 数字排名,
                        "is_unicorn": true/false, 
                        "market_value": "市值/估值（如有）"
                    }},
                    {{
                        "name": "{region_name}公司名称5",
                        "description": "公司描述",
                        "type": "公司类型",
                        "rank": 数字排名,
                        "is_unicorn": true/false, 
                        "market_value": "市值/估值（如有）"
                    }},
                    ...更多公司（至少10家主要公司）
                ],
                "talent_competition": "{region_name}人才竞争情况详细描述",
                "entry_barriers": ["{region_name}入行门槛1", "入行门槛2", ...],
                "unicorn_companies": [
                    {{
                        "name": "{region_name}独角兽公司名称1",
                        "founded_year": "成立年份",
                        "valuation": "估值",
                        "description": "公司描述",
                        "core_business": "核心业务"
                    }},
                    {{
                        "name": "{region_name}独角兽公司名称2",
                        "founded_year": "成立年份",
                        "valuation": "估值",
                        "description": "公司描述",
                        "core_business": "核心业务"
                    }},
                    {{
                        "name": "{region_name}独角兽公司名称3",
                        "founded_year": "成立年份",
                        "valuation": "估值",
                        "description": "公司描述",
                        "core_business": "核心业务"
                    }},
                    {{
                        "name": "{region_name}独角兽公司名称4",
                        "founded_year": "成立年份",
                        "valuation": "估值",
                        "description": "公司描述",
                        "core_business": "核心业务"
                    }},
                    {{
                        "name": "{region_name}独角兽公司名称5",
                        "founded_year": "成立年份",
                        "valuation": "估值",
                        "description": "公司描述",
                        "core_business": "核心业务"
                    }},
                    ...更多独角兽企业（至少8家独角兽企业）
                ]
            }}
            
            16. outlook: {region_name}行业前景详细分析（至少300字，包含市场趋势、技术演变、政策影响等方面）
            
            17. opportunities: ["{region_name}机遇1详细描述", "机遇2详细描述", ...] （至少3条）
            
            18. challenges: ["{region_name}挑战1详细描述", "挑战2详细描述", ...] （至少3条）
            
            19. future_predictions: [
                {{
                    "year": "预测年份",
                    "content": "{region_name}详细预测内容"
                }},
                ...更多预测
            ]
            
            20. recommended_resources: [
                {{
                    "name": "{region_name}资源名称",
                    "type": "资源类型（书籍/课程/网站/视频）",
                    "url": "链接地址",
                    "description": "资源描述"
                }},
                ...更多资源
            ]
            
            特别说明：
            1. 所有分析必须专注于{region_name}地区，而非中国市场
            2. 您必须使用中文回答，不要使用英文
            3. 货币单位使用{currency}，薪资格式为{salary_format}
            4. 城市、公司等信息必须是{region_name}当地的，不要混用中国数据
            
            请确保回复仅包含有效的JSON格式，不含其他文字说明。
            """
        
        try:
            # 设置超时参数，尤其对全球分析使用较短的超时时间
            timeout = 60 if region == "GLOBAL" else 120
            
            response = openai.chat.completions.create(
                model=self.config["llm_model"],
                messages=[
                    {"role": "system", "content": f"你是一位专业的行业分析师，精通{region_name}职业发展趋势分析。请务必使用中文回答。"},
                    {"role": "user", "content": prompt}
                ],
                temperature=0.7,
                timeout=timeout
            )
            
            result = response.choices[0].message.content
            
            # 检查结果是否为英文（简单判断）
            if len(result) > 100 and sum(1 for c in result[:100] if ord(c) < 128) / 100 > 0.9:
                # 结果可能是英文，尝试请求翻译
                logger.warning(f"检测到{region_name}分析结果可能为英文，尝试翻译...")
                
                try:
                    translated = openai.chat.completions.create(
                        model=self.config["llm_model"],
                        messages=[
                            {"role": "system", "content": "你是一位专业翻译，将英文专业内容翻译成中文。保持JSON格式不变，仅翻译内容。"},
                            {"role": "user", "content": f"请将以下JSON格式的行业分析内容翻译成中文，保持JSON格式不变:\n\n{result}"}
                        ],
                        temperature=0.3,
                        timeout=30
                    )
                    result = translated.choices[0].message.content
                    logger.info("翻译完成")
                except Exception as e:
                    logger.error(f"翻译过程出错: {str(e)}")
            
            # 尝试解析JSON
            try:
                json_result = json.loads(result)
                # 添加地区标记
                json_result['region'] = region
                return json_result
            except json.JSONDecodeError:
                # 可能返回的不是纯JSON，尝试提取JSON部分
                import re
                json_pattern = r'```json(.*?)```|{.*}'
                match = re.search(json_pattern, result, re.DOTALL)
                if match:
                    json_str = match.group(1) if match.group(1) else match.group(0)
                    json_result = json.loads(json_str)
                    json_result['region'] = region
                    return json_result
                else:
                    raise ValueError("无法解析LLM返回的结果为JSON格式")
                
        except Exception as e:
            logger.error(f"获取行业趋势分析时出错: {str(e)}")
            return {
                "industry": "信息技术",
                "description": f"无法获取{region_name}行业分析。错误信息：{str(e)}",
                "region": region,
                "hot_skills": ["Python", "数据分析", "机器学习"],
                "outlook": "服务暂时不可用，请稍后再试。"
            }
        
    def _extract_industry_from_job(self, job_description):
        """从职位描述中提取行业信息"""
        # 实现行业提取逻辑
        # 如果无法提取，返回一个默认行业
        return "信息技术"
        
    def _extract_skills_from_job(self, job_description):
        """从职位描述中提取技能关键词"""
        # 实现技能提取逻辑
        return ["编程", "数据分析", "机器学习"]
        
    def _get_industry_growth_data(self, industry):
        """获取行业增长数据"""
        # 实现获取行业增长数据的逻辑
        return {"growth_rate": 8.5, "market_size": "万亿级", "forecast": "稳定增长"}
        
    def _get_skill_trends(self, skills):
        """获取技能需求趋势"""
        # 实现获取技能趋势的逻辑
        return {skill: {"trend": "上升", "demand": "高"} for skill in skills}
        
    def _identify_hot_skills(self, skills, skill_trends):
        """识别热门技能"""
        # 实现热门技能识别逻辑
        return skills[:2]  # 简单地返回前两个技能
        
    def _identify_emerging_tech(self, industry):
        """识别新兴技术"""
        # 实现新兴技术识别逻辑
        return ["人工智能", "区块链", "云计算"]
        
    def _generate_industry_outlook(self, industry, growth_data):
        """生成行业前景分析"""
        # 实现行业前景分析生成逻辑
        return f"{industry}行业未来五年预计将保持{growth_data['growth_rate']}%的年均增长率，市场规模{growth_data['market_size']}，发展趋势{growth_data['forecast']}。" 